user rating prediction for yelp dataset.

1. Yelp Data Challenge
User Rating Prediction using
machine and Deep learning.
Amr Koura

2. Content
• Yelp Data Challenge
• Problem Definition
• Classical Machine Learning
• Deep Learning
• Docker Image
• Conclusion

3. Yelp Data Challenge

4. Yelp dataset
Academic Dataset.
Information about local business.
Five Json files:
1. business
2. check-in
3. review
4. tip
5. user
https://www.yelp.com/dataset_challenge/dataset

5. User Reviews

6. User Reviews
Jason Files contains user reviews:
Example:
{"user_id": "Iu6AxdBYGR4A0wspR9BYHA", "review_id":
"KPvLNJ21_4wbYNctrOwWdQ", "stars": 5, "date": "2014-02-13", "text":
"Excellent food. Superb customer service. I miss the mario machines
they used to have, but it's still a great place steeped in tradition.", "type":
"review", "business_id": "5UmKMjUEUNdYWqANhGckJw"}

7. Problem Definition

8. “Given a user Review
text , can we predict
user Rating?”

9. Simpler Problem
Assuming That:
• bad rate (1-3)
• good rate (4-5)
we will predict whether the user like the
service (rate=4 or 5) or not(rate=1,2 or 3)

10. “Given a user Review
text , can we predict
whether user like
service or not?”

11. Pre-Processing

12. Pre-processing
Extract the first 2000 review as dataset.
Features:
user reviews text.
Labels:
if Rate >3: Positive
Else: Negative

13. Approach

14. Classical
Machine
Learning
Algorithms
Deep learning
Approach

15. Classical
Machine Learning
Algorithms

16. Classical Machine Learning
combination of several machine learning algorithms.
Logistic Regression.
Naive Bayes.
Stochastic gradient Descent.
Support Vector Machine.
combine results using majority votes.
https://github.com/amrqura/yelpRatePrediction

17. Classical Machine Learning
Features:
use words of the review text as features as the following:
1.Extract most common 3000 words in the training dataset.
In each statement examine if the frequent words exists or not.
Feature set : matrix of [2000*3000]
2000: number of statements.
3000: boolean values examine existence of frequent word.
https://github.com/amrqura/yelpRatePrediction

18. Classical Machine Learning
Training:
Train model in 80% statements and validate on 20%.
Run:
$ python3 TextClassification.py
https://github.com/amrqura/yelpRatePrediction

19. Classical Machine Learning
Results(5- cross validation):
Naive Bayes accuracy percent: 66.5
MultinomialNB accuracy percent: 67.95
BernoulliNB accuracy percent: 65.9
Logistic regression accuracy percent: 67.15
SGD accuracy percent: 63.3
SVC accuracy percent: 54.0
LinearSVC accuracy percent: 63.75
NuSVC accuracy percent: 66.35
Voted accuracy percent: 67.35
https://github.com/amrqura/yelpRatePrediction

20. Deep learning
Approach

21. Convolutional Neural Network
for sentence classification

22. CNN Architecture
https://arxiv.org/pdf/1408.5882v2.pdf

23. CNN Dataset
Each statement is represented by n*k matrix.
n= number of words
k= vector length.
we use word2vec to convert each word to vector.
https://github.com/amrqura/deepYelpRatePrediction

24. CNN Training
implementation using Tensorflow.
number of filters=128
filter size=3,4,5.
drop out=0.5
Apply max pooling.
output layer: two nodes with softmax function.
use cross-entropy error function.
use L2 regularization.
https://github.com/amrqura/deepYelpRatePrediction

25. CNN
Training:
5 cross validation.
Train model in 1600 statements and validate on 400.
Run:
$ python rating_prediction.py
Accuracy: 0.73549998
https://github.com/amrqura/deepYelpRatePrediction

26. Docker Image

27. Docker Image
Docker Image:
https://hub.docker.com/r/amrkoura/yelpchallenge/
Run:
docker pull amrkoura/yelpchallange
docker run -t -i amrkoura/yelpchallenge /bin/bash
cd /src/
Run classical machine learning:
python3 TextClassification.py
Run Deep learning:
python rating_prediction.py

28. Conclusion

29. Conclusion
2 implementations to predict the user rating from user
review.
• classical machine learning
• Deep learning , convolutional neural network.
public Docker image:
https://hub.docker.com/r/amrkoura/yelpchallenge/

30. Thank you